1. Field of the Invention
The present invention generally relates to a method and system for contouring target volumes, more specifically relates to an efficient method and system for contouring target volumes and/or normal tissues at risk using an expert case as interactive tutorial reference for a radiation therapy treatment plan.
2. Description of the Related Art
Using a System for measuring myocardium in cardiac images is known. U.S. Pat. No. 5,669,382 to Curwen, et al. discloses a System for determining the epicardial boundary, being a closed curve dividing the myocardium from the tissue and blood surrounding the left ventricle. A mean and standard deviation is determined for pixels of a medical image of the subject's myocardial tissue. These are used to define a “goodness function” over the image which is positive for pixels statistically likely to be myocardial tissue, and negative for other pixels. An initial curve for modeling the epicardium in radial coordinates starts with a curve of inner myocardial boundary obtained my conventional imaging techniques. This curve is then iteratively updated to maximize the total “goodness function” of the region encompassed.
Using computerized software to modify developed treatment plan is known. U.S. Pat. No. 6,311,084 to Cormack, et al. discloses a computer based method and apparatus for providing prostate brachytherapy using Interventional Magnetic Resonance imaging is described. The invention utilizes the excellent soft tissue visualization that Interventional Magnetic Resonance provides to produce radiographic, geometric and dosimetric feedback to an implant treatment planner (software module). The feedback enables an iterative procedure for the placement of needles based upon cumulative dosimetric feedback on the effect of the current and prior needle placements. The invention allows a treatment plan to be developed and the implantation procedure to be performed initially in accordance with the developed treatment plan. Modifications to the plan are made in real-time by the invention software module coupled to the IMR imaging system. The implantation procedure continues with the modified plan where further modifications are made to the plan with placement of each subsequent needle. Calculation of final coverage is also provided for ending evaluation of the implant procedure.
Computer guided cryosurgery is known. U.S. Pat. No. 6,139,544 to Mikus, et al. discloses a system for assisting surgeons in performing cryosurgery of the prostate by calculating optimal positions for cryoprobes and providing display based templates for overlay over an ultrasound image display, and displaying actual cryoprobe ultrasound images together with template images so that the surgeon may compare suggested and actual placement of the cryoprobes, and adjust placement accordingly.
Computing contours in medical imaging system is known. U.S. Pat. No. 6,249,594 to Hibbard discloses a system and method is disclosed for automatically computing contours representing the boundaries of objects in three-dimensional tomographic images that may be formed by computed tomography (“CT”), magnetic resonance imaging (“MRI”), positron emission tomography (“PET”), single proton emission computed tomography (“SPECT”), or other appropriate methods. The system and method begin with a sample region of the object's interior and the single region is expanded in a step-wise fashion. At each step, a contour maximally matching the region's current edge, local gray-level gradient maxima, and prior contour shapes is determined. Upon completion of region expansion, the object contour is set to that step-contour having the maximum value of an objective function summing contributions from region edges, gradient edges, and prior shapes. Both the region expansion and the boundary contour determination are formulated such that there is a guaranteed average minimum error in the determination of the contours. This contour is represented as a parametric curve in which the contour size and shape are specified by the values of the parameters. These parameters are independent variables of the objective function. The parameters also are considered to be random variables capable of encoding a distribution of contour shapes, and by assuming a particular distribution, the contribution of shape constraints to the object function can be computed. The resulting contour corresponds to the set of parameters for which the objective function is a maximum.
Often in the course of clinical testament or diagnosis, a patient's internal anatomy is imaged for determining the extent to which disease has progressed. The diseased tissue may be evidenced by some variance from normal anatomy or function. Several imaging models are commonly used to generate images of the patient's anatomy and function for diagnostic and radiotherapy treatment purposes or surgical planning. These models include conventional X-ray plane film radiography; computed tomography (“CT”) imaging, magnetic resonance imaging (“MRI”); and nuclear medicine imaging techniques, such as positron emission tomography (“PET”) and single photon emission computed tomography (“SPECT”).
A common property shared by all the imaging models just mentioned is that all images are digitalized in that the images are represented as a set of numerical values representing a physical measurement. If these images are two-dimensional (“2-D”), the discrete picture elements are called pixels. However, if the images are three-dimensional (“3-D”), the discrete volume elements are referred to as voxels. For 3-D imaging models, single slices or sections are composed of pixels, but those same picture elements are equivalently termed voxels in a 3-D sense. The digital images from 2-D or 3-D imaging models are substantially exact maps of the pictured anatomy, so that each pixel value represents a sample of a property at a location in patient's coordinate system. Thus, the distances between pixel/voxel centers are proportional and have meaning in the sense of real physical spacing in the patient anatomy.
The numeric value of each pixel represents a sample of a property at that location. In CT images, for example, the numbers are a measure of relative X-ray absorbing power, e.g. spaces inside the lungs are usually pictured as dark (low CT number) while bone is generally bright (high CT number).
Alternatively, in the 2-D context of a slice or section, anatomy elements may be represented by 2-D templates identical in size and shape to the object 2-D templates are patterns of pixels all having the same value which represent a single region in an image. A representation by 2-D region-templates or by 2-D edge-contours are equivalent, since either representation can be readily computed from the other.
As can be seen, 3-D reconstructions of patient anatomy are most often prepared using computer graphics by manually drawing the individual contours on a contiguous set 2-D image slices or sections and then combining them. This method is referred to as contouring.
Contouring is very time-consuming and labor intensive. The time and labor necessary to use this method increases significantly with the number of image slices, and the number and sizes of the organs, tumors, etc. in the anatomical area of interest. The quality of the contouring and the later produced 3-D images, depend on the resolution and contrast of the 2-D images, and on the knowledge and judgment of the physician, scientist, or skilled professional performing the reconstruction.
Three-dimensional radiation therapy treatment planning (“RTTP”) is a medical procedure that currently makes the greatest use of 3-D reconstructions. This is even despite the labor and time required to contour the organs and tumors to generate a useful plan. In fact, the largest fraction of the plan preparation time involves contouring.
Another method that may be used for forming representations of organs, tumors, and the like is the segmentation method. Segmentation is the identification of image objects as distinct regions or segments of an image. This method also may be used to generate 3-D reconstructions of a patient's anatomy. A number of autosegmentation methods have been proposed in the prior art. These prior art methods may be separated into two principal types: (1) semi-automated segmentation methods in which physicians, technicians, or skilled professionals direct or provide some needed information which is used to produce detailed contours, and (2) fully automated segmentation methods in which a computer based program develops the segmentation without requiring any human intervention. These methods will be described in greater detail subsequently.
Fully automated, computed segmentation has been reported only for limited anatomic locations and/or narrowly-defined imaging protocols. In fully automated, computed segmentation system, an imaging modality is used to produce the original images. Models such as MRI are preferred because they produce images of soft tissue and display neighboring organs at higher contrast than X-ray based imaging. Further, MRI scanners can be set to produce images emphasizing proton density or different relaxation phenomena. Further, multi-modal MRI, in principle, can provide more information for each voxel.
The few fully automated, computed segmentation techniques is typically directed to the segmentation of the brain gray matter, white matter, and cerebrospinal fluid (“CSF”) spaces using multi-modality MRI. These approaches use statistical pattern recognition methods to distinguish the various materials.
A different strategy for fully automated, computed segmentation is to map a labeled atlas onto patient data by nonlinear transformations, referred to as warping. This technique will produce local correspondences between the atlas and individual patient anatomies despite inter-subject anatomic differences. Typically, a procedure in which an elastic model of the brain anatomy is driven by data-overlap probabilities to warp brain atlas images onto MRI slice or section images is provided. Segmentation occurs by associating the image voxels with atlas tissue-type labels.
Therefore, to improve the quality of contouring results, there is need for a method and system that allows users to contour a set of target volume contours based on guidance from a disease-matched expert case.